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Mathematical Problems in Engineering
Volume 2012, Article ID 786281, 18 pages
Research Article

CT Metal Artifact Reduction Method Based on Improved Image Segmentation and Sinogram In-Painting

1Laboratory of Image Science and Technology, Southeast University, Nanjing 210096, China
2Centre de Recherche en Information Biomedicale Sino-Francais (LIA CRIBs), 35042 Rennes, France
3Laboratoire Traitement du Signal et de l'Image (LTSI) INSERM U642, Université de Rennes I, 35042 Rennes Cedex, France
4Department of Radiology, General Hospital of Tianjin Medical University, Tianjing 300052, China
5Department of Radiology, Nanjing First Hospital Affiliated to Nanjing Medical University, Nanjing 210029, China

Received 21 February 2012; Revised 25 June 2012; Accepted 27 June 2012

Academic Editor: Fatih Yaman

Copyright © 2012 Yang Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The streak artifacts caused by metal implants degrade the image quality and limit the applications of CT imaging. The standard method used to reduce these metallic artifacts often consists of interpolating the missing projection data but the result is often a loss of image quality with additional artifacts in the whole image. This paper proposes a new strategy based on a three-stage process: (1) the application of a large-scale non local means filter (LS-NLM) to suppress the noise and enhance the original CT image, (2) the segmentation of metal artifacts and metallic objects using a mutual information maximized segmentation algorithm (MIMS), (3) a modified exemplar-based in-painting technique to restore the corrupted projection data in sinogram. The final corrected image is then obtained by merging the segmented metallic object image with the filtered back-projection (FBP) reconstructed image from the in-painted sinogram. Quantitative and qualitative experiments have been conducted on both a simulated phantom and clinical CT images and a comparative study has been led with Bal's algorithm that proposed a similar segmentation-based method.